🤖 Agents for Enterprise

Complete Enterprise Agentic AI Architectures with .NET

Build Production-Grade AI Systems for Senior .NET Architects & Tech Leads

🚀 Quick Start • 📚 Table of Contents • 🏗️ Architecture • 💬 Community

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🎯 Why This Book?

"You're a Senior Architect, but AI makes you feel like a Junior again."

You've tried to learn Agentic AI, but everything is in Python or focused on simple scripts that crumble in a real enterprise environment. You don't need another "Hello World" tutorial. You need to handle latency, security, and state management in a distributed C# system.

📋 This Book is For You If...

• ✅ You're a Software Developer, Architect, or Tech Lead working in enterprise environments
• ✅ You need to integrate AI agents into existing microservices and backend systems
• ✅ You want to build with .NET and C#—not switch to Python
• ✅ You require production-grade patterns for security, observability, and scalability
• ✅ You're adding AI features to legacy systems and need enterprise-ready solutions

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🏗️ What You'll Master

Capability	Description
Microsoft Agent Framework	Deep dive into Microsoft's official agent framework for building sophisticated AI systems
Multi-Agent Orchestration	Design and implement complex workflows with multiple cooperating agents
Agentic RAG Systems	Build retrieval-augmented generation systems that go beyond simple Q&A
Enterprise Security	Implement guardrails, PII redaction, rate limiting, and compliance controls
Observability & Tracing	Full OpenTelemetry integration with .NET Aspire for production monitoring
Azure Foundry Deployment	Deploy and scale your agent systems on Azure with confidence

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📚 What's Inside

This comprehensive guide takes you from foundational concepts to production-ready enterprise deployments across 8 parts and 26 chapters:

Part	Title	Description
I	Foundations	Understanding AI agents—why they matter, what they are, and their core components
II	Explore & Setup	Navigate the AI landscape and configure your .NET development environment
III	Core Development	Build agents with tools, memory, streaming, and structured outputs
IV	Multi-Agent	Design workflows with multiple cooperating agents and orchestration
V	Agentic RAG	Retrieval-augmented generation systems for enterprise knowledge
VI	Communications	A2A, MCP, AG-UI protocols for agent interoperability
VII	Advanced	Middleware, skills architecture, and advanced reasoning patterns
VIII	Enterprise	ADLC, observability, security, deployment, and cost management

📖 View All 26 Chapters

Part I: Foundations of AI Agents

Chapter	Topic	Description
01	Why Agents	Understanding the motivation and strategic value of AI agents in enterprise
02	AI Agents	What AI agents are and how they differ from traditional software systems
03	Components	Core architectural components that make up a production AI agent

Part II: Explore AI Landscape and Setup Agent Environment

Chapter	Topic	Description
04	Frameworks	Overview of agent frameworks—Microsoft Agent Framework, Semantic Kernel, and more
05	Ecosystem	The broader AI agent ecosystem, tools, and enterprise integrations
06	Setup	Setting up your .NET development environment for building agents

Part III: Core Agent Development—Mechanics, Tools, and Memory

Chapter	Topic	Description
07	Getting Started	Hands-on guide to building your first AI agent with streaming and structured outputs
08	Tool Use	Function calling, code interpretation, file search, and web search capabilities
09	Memory	Session management, context providers, and persistent memory with Cosmos DB

Part IV: Multi-Agent Workflows and Orchestration

Chapter	Topic	Description
10	Workflows	Agentic workflow patterns, checkpoints, and human-in-the-loop orchestration

Part V: Agentic RAG (Retrieval-Augmented Generation)

Chapter	Topic	Description
11	Agentic RAG Concepts	Understanding agentic RAG architecture and design patterns
12	Develop Agentic RAG	Building RAG systems with Qdrant vector stores and enterprise data sources

Part VI: Agent Communications and Protocols

Chapter	Topic	Description
13	Communications	Agent communication strategies and protocol design
14	Agent-to-Agent (A2A)	Building enterprise compliance services with agent-to-agent protocols
15	Model Context Protocol (MCP)	Implementing MCP with GitHub integration and governance examples
16	AG-UI Protocol	Agent-to-UI communication patterns with client-server architecture
17	Developer UI	Building enterprise KYC services with developer-friendly interfaces

Part VII: Advanced Agent Development—Middleware, Skills, and Reasoning

Chapter	Topic	Description
18	Middleware	Enterprise middleware patterns—guardrails, PII redaction, rate limiting
19	Skills	Enterprise skills architecture for composable agent capabilities
20	Reasoning	Advanced reasoning patterns for complex decision-making agents

Part VIII: Enterprise Considerations—ADLC, Observability, Evaluations, and Security

Chapter	Topic	Description
21	ADLC	Agent Development Lifecycle—from design to production
22	Observability	Full observability with .NET Aspire and OpenTelemetry
23	Evaluation	Testing, evaluation frameworks, and quality assurance for AI agents
24	Security	Enterprise security architecture and compliance patterns
25	Deployment	Azure Foundry deployment strategies and cloud-native patterns
26	Cost Management	Cost optimization strategies for enterprise AI systems

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🛠️ Technology Stack

Category	Technologies
Languages & Frameworks	.NET 10 • C# • ASP.NET Core
AI & Agents	Microsoft Agent Framework • Semantic Kernel • Azure OpenAI
Cloud & Deployment	Azure Foundry • Azure Container Apps • Azure Kubernetes Service
Observability	.NET Aspire • OpenTelemetry • Application Insights
Data & Storage	Cosmos DB • Qdrant • Azure Blob Storage
Architecture	Microservices • Clean Architecture • Domain-Driven Design

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🚀 Quick Start

Prerequisites

• .NET 10 SDK
• Visual Studio 2026 or VS Code
• Azure Subscription (for cloud deployments)
• Docker Desktop (for local development)

Clone and Run

# Clone the repository
git clone https://github.com/mehmetozkaya/agents-on-enterprise.git
cd agents-on-enterprise

# Navigate to your first agent
cd part-02-explore/06-setup/HelloAgent

# Set environment variables (PowerShell)
$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"  # Replace with your Azure OpenAI resource endpoint
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5-mini"  # Optional, defaults to gpt-5-mini

# Run the agent
dotnet run

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📐 Architecture Patterns

This repository demonstrates enterprise-grade architectural patterns including:

┌─────────────────────────────────────────────────────────────────┐
│                    Enterprise Agent Architecture                │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │   Agent 1   │  │   Agent 2   │  │   Agent N   │   Agents     │
│  │  (Planner)  │  │ (Executor)  │  │  (Expert)   │              │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘              │
│         │                │                │                     │
│  ┌──────┴────────────────┴────────────────┴──────┐              │
│  │              Orchestration Layer              │   Workflow   │
│  │     (Multi-Agent Coordination & Routing)      │              │
│  └──────────────────────┬───────────────────────-┘              │
│                         │                                       │
│  ┌──────────────────────┴───────────────────────┐               │
│  │              Middleware Pipeline             │   Security    │
│  │  (Guardrails • PII • Rate Limiting • Auth)   │               │
│  └──────────────────────┬───────────────────────┘               │
│                         │                                       │
│  ┌──────────────────────┴───────────────────────┐               │
│  │            Enterprise Services               │   Backend     │
│  │    (RAG • Memory • Tools • Integrations)     │               │
│  └──────────────────────┬───────────────────────┘               │
│                         │                                       │
│  ┌──────────────────────┴───────────────────────┐               │
│  │              Observability Layer             │   Ops         │
│  │   (OpenTelemetry • Tracing • Metrics)        │               │
│  └──────────────────────────────────────────────┘               │
└─────────────────────────────────────────────────────────────────┘

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💬 Community

• 🌐 Website: agentsonenterprise.com
• 📧 Contact: Questions? Reach out through the website
• ⭐ Star this repo if you find it valuable!

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📄 License

This repository contains code samples and examples from the Agents for Enterprise book. The code is provided for educational purposes.

For the complete learning experience including detailed explanations, architectural diagrams, and enterprise patterns, get the full book.

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Ready to Build Production-Grade AI Agent Systems?

Stop translating Python scripts. Start building enterprise AI with .NET.

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